On Binary Distributed Hypothesis Testing

TL;DR

This paper investigates the fundamental limits of distributed binary hypothesis testing over rate-limited links, providing new achievability bounds and analyzing the tradeoff between error exponents and communication rates for different source correlation scenarios.

Contribution

It offers improved achievability results for the side-information setting and extends the analysis to symmetric rate constraints using Korner-Marton coding.

Findings

Tighter analysis of binning error effects improves error exponent tradeoff results.

Full characterization of the exponent tradeoff for the side-information setting.

Korner-Marton coding achieves near-optimal performance under symmetric rate constraints.

Abstract

We consider the problem of distributed binary hypothesis testing of two sequences that are generated by an i.i.d. doubly-binary symmetric source. Each sequence is observed by a different terminal. The two hypotheses correspond to different levels of correlation between the two source components, i.e., the crossover probability between the two. The terminals communicate with a decision function via rate-limited noiseless links. We analyze the tradeoff between the exponential decay of the two error probabilities associated with the hypothesis test and the communication rates. We first consider the side-information setting where one encoder is allowed to send the full sequence. For this setting, previous work exploits the fact that a decoding error of the source does not necessarily lead to an erroneous decision upon the hypothesis. We provide improved achievability results by carrying out a tighter analysis of the effect of binning error; the results are also more complete as they cover the full exponent tradeoff and all possible correlations. We then turn to the setting of symmetric rates for which we utilize Korner-Marton coding to generalize the results, with little degradation with respect to the performance with a one-sided constraint (side-information setting).